Learning unknown physics of non-Newtonian fluids
Learning unknown physics of non-Newtonian fluids
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DOI：
10.1103/physrevfluids.6.073301
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发表时间：
2021-07-09
影响因子：
2.7
通讯作者：
Tartakovsky, Alexandre M.
中科院分区：
文献类型：
--
作者：
Reyes, Brandon;Howard, Amanda A.;Tartakovsky, Alexandre M.
我们提出了一个制定的物理通知神经网络（PINN）的方法学习的有效粘度的广义牛顿流体的速度和压力的测量随时间变化的三维流动，并将其应用于估计粘度模型的两个非牛顿系统（聚合物熔体和悬浮液的颗粒）在两个平行板之间的剪切流仅使用速度测量数值模拟。PINN推断的粘度模型同意经验模型的剪切速率与大的绝对值，但偏离剪切速率接近零的经验模型有一个非物理的奇异性。我们表明，一旦未知的物理学是学习的PINN方法可以用来解决的动量守恒方程的非牛顿流体的流动。
We present a formulation of the physics-informed neural network (PINN) method for learning the effective viscosity of the generalized Newtonian fluid from measurements of velocity and pressure in time-dependent three-dimensional flows and apply it to estimating viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) in shear flow between two parallel plates using only velocity measurements from numerical simulations. The PINN-inferred viscosity models agree with empirical models for shear rates with large absolute values but deviate for shear rates near zero where empirical models have an unphysical singularity. We show that once the unknown physics is learned the PINN method can be used to solve the momentum conservation equation governing flow of non-Newtonian fluids.